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Computerized adaptive testing to screen pre-school children for emotional and behavioral problems
Meinou H C Theunissen1, Iris Eekhout2, Sijmen A Reijneveld2,3
1Child Health, TNO (Netherlands Organisation of Applied Scientific Research), Leiden, The Netherlands. Meinou.Theunissen@tno.nl.
Insights
Computerized adaptive testing (CAT) offers an efficient and valid method for identifying emotional and behavioral (EB) problems in preschool children, improving upon traditional questionnaires.
Area of Science:
- Child Psychology
- Developmental Pediatrics
- Psychometric Methods
Background:
- Traditional questionnaires for detecting emotional and behavioral (EB) problems in preventive child healthcare (PCH) are often short, potentially compromising validity and reliability.
- Computerized adaptive testing (CAT) presents a potential solution to enhance the efficiency and accuracy of EB problem detection.
Purpose of the Study:
- To develop a CAT for measuring EB problems in preschool children.
- To assess the efficiency and validity of the developed CAT for identifying EB problems.
Main Methods:
- Utilized a Dutch national dataset of 2192 parents of preschool children undergoing PCH assessments.
- Developed a CAT using 80% of the data to calculate item parameters and define EB problem cutoffs.
- Assessed CAT validity and efficiency using simulation techniques against the Child Behavior Checklist (CBCL) as a criterion.
Main Results:
- A CAT was developed, meeting item criteria with 193 items.
- The CAT required an average of 16 items to identify children with EB problems.
- High sensitivity (0.89-0.94) and specificity (0.91-0.93) were achieved for total, emotional, and behavioral problems compared to CBCL scores.
Conclusions:
- A CAT is a highly promising tool for the efficient and high-quality identification of EB problems in preschool children.
- Further validation through real-life administration of the CAT is recommended.
Abstract:
Questionnaires to detect emotional and behavioral (EB) problems in preventive child healthcare (PCH) should be short; this potentially affects their validity and reliability. Computerized adaptive testing (CAT) could overcome this weakness. The aim of this study was to (1) develop a CAT to measure EB problems among pre-school children and (2) assess the efficiency and validity of this CAT. We used a Dutch national dataset obtained from parents of pre-school children undergoing a well-child care assessment by PCH (n = 2192, response 70%). Data regarded 197 items on EB problems, based on four questionnaires, the Strengths and Difficulties Questionnaire (SDQ), the Child Behavior Checklist (CBCL), the Ages and Stages Questionnaire: Social Emotional (ASQ:SE), and the Brief Infant-Toddler Social and Emotional Assessment (BITSEA). Using 80% of the sample, we calculated item parameters necessary for a CAT and defined a cutoff for EB problems. With the remaining part of the sample, we used simulation techniques to determine the validity and efficiency of this CAT, using as criterion a total clinical score on the CBCL. Item criteria were met by 193 items. This CAT needed, on average, 16 items to identify children with EB problems. Sensitivity and specificity compared to a clinical score on the CBCL were 0.89 and 0.91, respectively, for total problems; 0.80 and 0.93 for emotional problems; and 0.94 and 0.91 for behavioral problems. Conclusion: A CAT is very promising for the identification of EB problems in pre-school children, as it seems to yield an efficient, yet high-quality identification. This conclusion should be confirmed by real-life administration of this CAT. What is Known: • Studies indicate the validity of using computerized adaptive test (CAT) applications to identify emotional and behavioral problems in school-aged children. • Evidence is as yet limited on whether CAT applications can also be used with pre-school children. What is New: • The results of this study show that a computerized adaptive test is very promising for the identification of emotional and behavior problems in pre-school children, as it appears to yield an efficient and high-quality identification.
